标题： CP-扩张：一种保留组织病理学图像边界上下文信息的复制粘贴增强方法 显示英文标题

标题： CP-Dilatation: A Copy-and-Paste Augmentation Method for Preserving the Boundary Context Information of Histopathology Images

摘要： 医学人工智能诊断，包括组织病理学分割，得益于深度学习技术的最新发展。 然而，深度学习本身需要大量训练数据，特别是医学图像分割掩码，由于医学专家的短缺，需要极高的成本。 为缓解这一问题，我们提出了一种基于传统复制粘贴（CP）增强技术的新数据增强方法，称为CP-Dilatation，并将其应用于组织病理学图像分割。 对于众所周知的传统CP技术，所提出的方法添加了一个膨胀操作，可以在恶性肿瘤的边界上下文信息得到保留，这在组织病理学图像诊断中非常重要，因为恶性肿瘤与其边缘之间的边界大多不清晰，且边缘存在显著的上下文信息。 在使用组织病理学基准数据集的实验中，所提出的方法被发现优于其他用于比较的最先进基线方法。 显示英文摘要

摘要： Medical AI diagnosis including histopathology segmentation has derived benefits from the recent development of deep learning technology. However, deep learning itself requires a large amount of training data and the medical image segmentation masking, in particular, requires an extremely high cost due to the shortage of medical specialists. To mitigate this issue, we propose a new data augmentation method built upon the conventional Copy and Paste (CP) augmentation technique, called CP-Dilatation, and apply it to histopathology image segmentation. To the well-known traditional CP technique, the proposed method adds a dilation operation that can preserve the boundary context information of the malignancy, which is important in histopathological image diagnosis, as the boundary between the malignancy and its margin is mostly unclear and a significant context exists in the margin. In our experiments using histopathology benchmark datasets, the proposed method was found superior to the other state-of-the-art baselines chosen for comparison.